Approximate Bayesian Inference via Sparse Grid Quadrature Evaluation (BISQuE) for Hierarchical Models

Implementation of the 'bisque' strategy for approximate Bayesian posterior inference. See Hewitt and Hoeting (2019) for complete details. 'bisque' combines conditioning with sparse grid quadrature rules to approximate marginal posterior quantities of hierarchical Bayesian models. The resulting approximations are computationally efficient for many hierarchical Bayesian models. The 'bisque' package allows approximate posterior inference for custom models; users only need to specify the conditional densities required for the approximation.


bisque 1.0.1 (2019-04-25)

Bug fixes

  • Fixing buffer overflow in spatial composition sampler.

Reference manual

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1.0.2 by Joshua Hewitt, a year ago

Browse source code at

Authors: Joshua Hewitt

Documentation:   PDF Manual  

GPL-3 license

Imports mvQuad, Rcpp, foreach, itertools

Suggests testthat, fields

Linking to Rcpp, RcppArmadillo, RcppEigen

System requirements: A system with a recent-enough C++11 compiler (such as g++-4.8 or later).

See at CRAN